The Lifecycle of an Analytics Project.Blogs
We believe every project should start with the definition of the goals of the business and project.
You might think that every business has defined its goals which it tries to achieve. However, most of the time that’s not the case. And even if goals are defined, they might be defined badly. A prime example of an incorrect goal is something like:
“We want more visitors, leads and sales.”
When you hear a goal like this, a lot of questions should pop into your mind:
What does ‘more’ mean?
- In what time frame?
- From which channels?
- What are our resources to achieve this?
The list can go on and on. A better defined goal would look like this:
“We need 20,000 visitors, 500 leads, and 12 customers within the next 12-months from our inbound marketing efforts in order to achieve our revenue goal of $600,000 from inbound marketing.”
The difference between these two goals is that the second one is SMART and the first one is not. What does SMART mean?
Every letter represents one quality which every goal should have. So every goal should be:
- Time Based
To create a measurement plan we need to:
- Define KPIs,
- Define interactions, which have to be tracked so we are able to measure those KPIs (and other metrics).
KPIs (Key Performance Indicators) are key metrics which indicate how are we doing in completing our goals.
Now, when we know which metrics are the most important, we need to make sure that we are going to track everything that is needed to measure those metrics.
In case our goal is to increase revenue, our KPIs can be revenue and number of transactions. In order to measure these we have to make sure that we can track transactions which are happening on our site.
The next step is to prepare an analytics scan. We make this scan mainly for the new clients: we get a better idea about their Google Analytics account and Google Tag Manager container. Also it helps us to determine what interactions are already tracked and what further tracking needs to be implemented.
First, we have to make sure that a Google Analytics account is set up properly. Also, we need to check if the data which is collected is consistent and if it is the best quality possible. Some problems can be solved directly in Google Analytics by setting up a new filter or tweaking other settings. Other bigger problems are solved in Google Tag Manager container.
Even though we try to achieve the best quality of data, it’s important to realise that perfect data doesn’t exist. We are limited by technology or by the way Google Analytics (or other tools) collects and reports data. However, we have to make sure that the data is in the best shape possible so we can use it later in analysis.
Data Collection and Implementation Plan
After completing the analytics scan we know what additional tracking needs to be implemented.
Based on the implementation difficulty and the client’s resources we decide if:
- the whole implementation will be handled by us in Google Tag Manager,
- or we will need a web developer to add additional tracking code to the website.
In some cases the input of the web developer is a must, especially when it comes to adding enhanced e-commerce tracking or other more advanced custom trackings.
After the implementation, testing and debugging, the process of data collection starts.
Data Analysis is a really broad topic. Also, it’s really difficult to generalise it, because it differs from a client to client and from a project to project.
Most of the time, at the start of the analysis project, there is a question.
“So, how is my site performing?”
We aren’t the biggest fans of questions like this for a simple reason: it’s almost impossible to answer this question. This question poses the same problem as the non-SMART goal above. Also, the answer to a question like this doesn’t initiate any action.
The following graph describes the problem very well:
The difficulty of analysis is displayed on the x-axis and the value of the analysis on the y-axis.
In the lower part of the graph (low difficulty – low value) are analyses which describe things that already happened or why those things happened. The value of analysis which tries to answer questions like “What happened?” or “Why did it happen?” is low because the analysis doesn’t initiate any action. Of course it’s important to know the answer to questions like this as well, but answering these questions shouldn’t take the majority of time allocated to analysis.
As the difficulty of analysis (or question) increases, the value of the result goes up. In this part there are questions like “What will happen?” or “How can we make it happen?”
Questions like these are much better because they drive action.
So the question which initializes analysis is crucial, because the question predetermines the value of the answer. This is also an important part of the work of a digital analyst: to teach how to ask the right questions in order to put main focus on the more challenging and important ones, so that the answers can provide real value.
Data Visualisation and Reporting
After the analysis is done we need to present the results to the client. Most of the time, we use Google Data Studio for creating dashboards and reports.
It’s a tool made by Google and its main purpose is to create interactive reports. The main advantage of this tool is that it’s directly connected to all other Google tools, such as Google Analytics, Search Console, Google Sheets and so on. Connection with Google Sheets is really nice, because it provides an option to import data from other external sources as well, for example from a backend system or CRM.
When creating a report or dashboard it’s important to realise that it’s not about creating nice graphs, tables and numbers. The main reason why the report is created is because it contains a quick insight, statement, or explanation of the analyst who made the analysis/report. All the numbers and graphs are there to support this statement. It is important to tell a story and not just show numbers because alone they don’t have any value at all.
Getting Insights and Action
This part is connected to the previous one: the result of the analysis has to be action. Because if there is no action after analysis nothing will ever change and business will never be data-driven. Moreover, all the work that was done before is almost useless.